Abstract
In this paper we introduce model-based search as a unifying framework accommodating some recently proposed heuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, cross-entropy and estimation of distribution methods. We discuss similarities as well as distinctive features of each method, propose some extensions and present a comparative experimental study of these algorithms.
Cite
CITATION STYLE
Zlochin, M., & Dorigo, M. (2002). Model-based search for combinatorial optimization: A comparative study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2439, pp. 651–661). Springer Verlag. https://doi.org/10.1007/3-540-45712-7_63
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